Deriving a Point-Spread Function in a Crowded Field

following Appendix III of Peter Stetson’s User’s Manual for DAOPHOT II

Using pydaophot form astwro python package

All italic text here have been taken from Stetson’s manual.

The only input file for this procedure is a FITS file containing reference frame image. Here we use sample FITS form astwro package (NGC6871 I filter 20s frame). Below we get filepath for this image, as well as create instances of Daophot and Allstar classes - wrappers around daophot and allstar respectively.

One should also provide daophot.opt, photo.opt and allstar.opt in apropiriete constructors. Here default, build in, sample, opt files are used.

from astwro.sampledata import fits_image
frame = fits_image()

Daophot object creates temporary working directory (runner directory), which is passed to Allstar constructor to share.

from astwro.pydaophot import Daophot, Allstar
dp = Daophot(image=frame)
al = Allstar(dir=dp.dir)

Daophot got FITS file in construction, which will be automatically ATTACHed.

(1) Run FIND on your frame

Daophot FIND parameters Number of frames averaged, summed are defaulted to 1,1, below are provided for clarity.

res = dp.FInd(frames_av=1, frames_sum=1)

Check some results returned by FIND, every method for daophot command returns results object.

print ("{} pixels analysed, sky estimate {}, {} stars found.".format(res.pixels,, res.stars))
9640 pixels analysed, sky estimate 12.665, 4166 stars found.

Also, take a look into runner directory

!ls $dp.dir
63d38b_NGC6871.fits   allstar.opt      daophot.opt    i.coo

We see symlinks to input image and opt files, and i.coo - result of FIND

(2) Run PHOTOMETRY on your frame

Below we run photometry, providing explicitly radius of aperture A1 and IS, OS sky radiuses.

res = dp.PHotometry(apertures=[8], IS=35, OS=50)

List of stars generated by daophot commands, can be easily get as astwro.starlist.Starlist being essentially pandas.DataFrame:

stars = res.photometry_starlist

Let’s check 10 stars with least A1 error (mag_err column). (pandas style)

id x y mag sky sky_err sky_skew mag_err
2631 2631 982.57 733.50 12.430 12.626 2.27 0.08 0.0012
2387 2387 577.37 666.48 12.118 15.649 6.55 0.52 0.0012
391 391 702.67 102.05 12.533 12.755 2.45 0.08 0.0012
697 697 502.64 177.66 12.741 12.794 2.41 0.09 0.0014
879 879 1091.86 226.61 12.841 12.902 2.48 0.10 0.0014
926 926 1107.02 241.15 12.763 12.866 2.43 0.11 0.0014
2277 2277 1165.50 636.91 12.742 12.567 2.36 0.08 0.0014
3681 3681 935.70 1025.92 13.129 12.528 2.28 0.07 0.0017
1753 1753 223.25 481.61 13.170 12.513 2.18 0.03 0.0017
2364 2364 603.22 662.33 12.908 16.590 5.20 0.43 0.0018

(3) SORT the output from PHOTOMETRY

in order of increasing apparent magnitude decreasing stellar brightness with the renumbering feature. This step is optional but it can be more convenient than not.

SORT command of daophor is not implemented (yet) in pydaohot. But we do sorting by ourself.

sorted_stars = stars.sort_values('mag')

Here we write sorted list back info photometry file at default name (overwriting existing one), because it’s convenient to use default files in next commands.

dp.write_starlist(sorted_stars, 'i.ap')

(4) PICK to generate a set of likely PSF stars

How many stars you want to use is a function of the degree of variation you expect and the frequency with which stars are contaminated by cosmic rays or neighbor stars. […]

pick_res = dp.PIck(faintest_mag=20, number_of_stars_to_pick=40)

If no error reported, symlink to image file (renamed to i.fits), and all daophot output files (i.*) are in the working directory of runner:

ls $dp.dir
63d38b_NGC6871.fits       daophot.opt         i.coo
allstar.opt               i.ap                 i.lst

One may examine and improve i.lst list of PSF stars. Or use to obtain list of PSF stars optimised by genetic algorithm.

(5) Run PSF

tell it the name of your complete (sorted renumbered) aperture photometry file, the name of the file with the list of PSF stars, and the name of the disk file you want the point spread function stored in (the default should be fine) […]

If the frame is crowded it is probably worth your while to generate the first PSF with the “VARIABLE PSF” option set to -1 — pure analytic PSF. That way, the companions will not generate ghosts in the model PSF that will come back to haunt you later. You should also have specified a reasonably generous fitting radius — these stars have been preselected to be as isolated as possible and you want the best fits you can get. But remember to avoid letting neighbor stars intrude within one fitting radius of the center of any PSF star.

For illustration we will set VARIABLE PSF option, before PSf()

dp.set_options('VARIABLE PSF', 2)
psf_res = dp.PSf()

(6) Run GROUP and NSTAR or ALLSTAR on your NEI file

If your PSF stars have many neighbors this may take some minutes of real time. Please be patient or submit it as a batch job and perform steps on your next frame while you wait.

We use allstar. (GROUP and NSTAR command are not implemented in current version of pydaophot). We use prepared above Allstar object: al operating on the same runner dir that dp.

As parameter we set input image (we haven’t do that on constructor), and nei file produced by PSf(). We do not remember name i.psf so use psf_res.nei_file property.

Finally we order allstar to produce subtracted FITS .

alls_res = al.ALlstar(image_file=frame, stars=psf_res.nei_file, subtracted_image_file='is.fits')

All result objects, has get_buffer() method, useful to lookup unparsed daophot or allstar output:

print (alls_res.get_buffer())

                                     Picture size:   1250  1150

   File with the PSF (default 63d38b_NGC6871.psf):             Input file (default 63d38b_NGC6871.ap):                   File for results (default i.als):             Name for subtracted image (default is):
    915 stars.  <<

I = iteration number

R = number of stars that remain

D = number of stars that disappeared

C = number of stars that converged

     I       R       D       C
     1     915       0       0  <<
     2     915       0       0  <<
     3     915       0       0  <<
     4     724       0     191  <<
     5     385       0     530  <<
     6     211       0     704  <<
     7     110       0     805  <<
     8      67       0     848  <<
     9      40       0     875  <<
    10       0       0     915

    Finished i                                       

Good bye.

(8) EXIT from DAOPHOT and send this new picture to the image display

Examine each of the PSF stars and its environs. Have all of the PSF stars subtracted out more or less cleanly, or should some of them be rejected from further use as PSF stars? (If so use a text editor to delete these stars from the LST file.) Have the neighbors mostly disappeared, or have they left behind big zits? Have you uncovered any faint companions that FIND missed?[…]

The absolute path to subtracted file (like for most output files) is available as result’s property:

sub_img = alls_res.subtracted_image_file

We can also generate region file for psf stars:

from astwro.starlist.ds9 import write_ds9_regions
reg_file_path = dp.file_from_runner_dir('lst.reg')
write_ds9_regions(pick_res.picked_starlist, reg_file_path)
# One can run ds9 directly from notebook:
!ds9 $sub_img -regions $reg_file_path

(9) Back in DAOPHOT II ATTACH the original picture and run SUBSTAR

specifying the file created in step (6) or in step (8f) as the stars to subtract, and the stars in the LST file as the stars to keep.

Lookup into runner dir:

ls $al.dir
63d38b_NGC6871.fits       i.ap                 i.nei
allstar.opt               i.coo                i.psf
daophot.opt               i.err                is.fits
i.als                     i.lst                lst.reg
sub_res = dp.SUbstar(subtract=alls_res.profile_photometry_file, leave_in=pick_res.picked_stars_file)

You have now created a new picture which has the PSF stars still in it but from which the known neighbors of these PSF stars have been mostly removed

(10) ATTACH the new star subtracted frame and repeat step (5) to derive a new point spread function


for i in range(3):
    print ("Iteration {}: Allstar chi: {}".format(i, alls_res.als_stars.chi.mean()))
    dp.image = 'is.fits'
    alls_res = al.ALlstar(image_file=frame, stars='i.nei')
    dp.image = frame
    dp.SUbstar(subtract='i.als', leave_in='i.lst')
print ("Final:       Allstar chi: {}".format(alls_res.als_stars.chi.mean()))
Iteration 0: Allstar chi: 1.14670601093
Iteration 1: Allstar chi: 1.13409726776
Iteration 2: Allstar chi: 1.1332852459
Final:       Allstar chi: 1.13326229508

Check last image with subtracted PSF stars neighbours.

!ds9 $dp.SUbstar_result.subtracted_image_file -regions $reg_file_path

Once you have produced a frame in which the PSF stars and their neighbors all subtract out cleanly, one more time through PSF should produce a point-spread function you can be proud of.

dp.image = 'is.fits'
psf_res = dp.PSf()
print ("PSF file: {}".format(psf_res.psf_file))
PSF file: /var/folders/kt/1jqvm3s51jd4qbxns7dc43rw0000gq/T/pydaophot_tmpBVHrtR/i.psf